PREDICTION THE YIELD OF GRAIN CROPS USING BASIC MACHINE LEARNING ALGORITHMS

Dilmurod Khasanov; Nodir Rakhimov; Hojiakbar Abdulhakimov

Authors

Dilmurod Khasanov Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Nodir Rakhimov Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Abdulhakimov Hojiakbar Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Keywords:

Machine Learning, Root Mean Squared Error, Mean Absolute Error, Mean Squared Error, Linear Regression, Random Forest Regressor, Decision Tree

Abstract

This article presents the development of an AI model and a software tool designed to predict the yield of grain crops using Machine Learning (ML) algorithms and a dataset from kaggle.com. The research focuses on analyzing a variety of environmental, climatic, and agricultural factors that influence crop productivity. By leveraging regression techniques, the model aims to provide accurate yield forecasts based on historical data and real-time inputs. The software tool developed offers a user-friendly interface for farmers and agricultural professionals, enabling them to make informed decisions regarding resource management, crop selection, and harvest planning. The model effectiveness is evaluated through empirical testing such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE) highlighting its potential for improving agricultural efficiency and food security.

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